library(tidyverse)
chart_melt <- read_csv("chart_melt.csv")
Parsed with column specification:
cols(
  YEAR = col_double(),
  variable = col_character(),
  weather_index = col_double(),
  weather_value = col_double(),
  metric = col_character()
)
library(htmlwidgets)
library(plotly)

plot1 <- ggplot(chart_melt, aes(x=YEAR, y=weather_index, colour=variable)) +
          geom_point(aes(text=paste(metric,":", weather_value))) +
          geom_line() +
          facet_grid(~variable) +
          xlab("Year") + ylab("Weather Index") + ggtitle("Australia Weather Metrics 2010-2019") + 
          theme_minimal() +
          theme(legend.position = "none",
                plot.title = element_text(face = "bold", size = 20, hjust = 0.5), 
                axis.text.x = element_text(angle = 60, size = 8, hjust = 10),
                axis.title.x = element_text(vjust = 0.5)) +
          scale_x_continuous(breaks = c(2010, 2012, 2014, 2016, 2018))
Ignoring unknown aesthetics: text
ggplotly(plot1, tooltip = "text")
dt <- read_csv("dt.csv")
Parsed with column specification:
cols(
  WEEK = col_date(format = ""),
  avg_temp = col_double(),
  sum_precip = col_double(),
  avg_humidity = col_double(),
  max_wind = col_double()
)
library(DT)

FireSeason19_20 <- dt %>% datatable(rownames = FALSE, 
                                    colnames = c("Week", "Average Temperature (C)", "Total Precipitation (mm)", 
                                           "Average Humidity", "Max Wind (m/s)"),
                                    filter = c("top"), options = list(language = list(sSearch = "Filter:"))) %>%
                          formatRound(c('avg_temp', 'sum_precip', 'avg_humidity', 'max_wind'), 0)

FireSeason19_20 <- FireSeason19_20 %>% formatStyle('avg_temp',
                                                    background = styleColorBar(dt$avg_temp, 'tomato'),
                                                    backgroundPosition = 'right') %>%
                                       formatStyle('sum_precip',
                                                    background = styleColorBar(dt$sum_precip, 'lightblue'),
                                                    backgroundPosition = 'right') %>%
                                       formatStyle('avg_humidity',
                                                    background = styleColorBar(dt$avg_humidity, 'navajowhite'),
                                                    backgroundPosition = 'right') %>%
                                       formatStyle('max_wind',
                                                    background = styleColorBar(dt$max_wind, 'lightgrey'),
                                                    backgroundPosition = 'right')
FireSeason19_20

NA
combined <- read_csv("combined.csv")
Parsed with column specification:
cols(
  POSTAL_CODE = col_character(),
  lat = col_double(),
  lon = col_double(),
  temp_change = col_character(),
  precip_change = col_character(),
  humidity_change = col_character(),
  wind_change = col_character()
)
Warning messages:
1: In get(results[[i]], packages[[i]]) :
  restarting interrupted promise evaluation
2: In get(results[[i]], packages[[i]]) :
  internal error -3 in R_decompress1
#changing the buckets for each group
combined$temp_change[combined$temp_change > 25] <- "+25% to +50%"
combined$temp_change[combined$temp_change > 10 & combined$temp_change <= 25] <- "+10% to +25%"
combined$temp_change[combined$temp_change > 0  & combined$temp_change <= 10] <- "flat to +10%"
combined$temp_change[combined$temp_change == -1] <- "flat to -10%"
combined$temp_change[combined$temp_change == -3] <- "flat to -10%"
combined$temp_change[combined$temp_change == 9] <- "flat to +10%"
combined$temp_change[combined$temp_change == 8] <- "flat to +10%"
combined$temp_change[combined$temp_change == 7] <- "flat to +10%"
combined$temp_change[combined$temp_change == 6] <- "flat to +10%"
combined$temp_change[combined$temp_change == 5] <- "flat to +10%"
combined$temp_change[combined$temp_change == 4] <- "flat to +10%"
combined$temp_change[combined$temp_change == 3] <- "flat to +10%"

combined$precip_change[combined$precip_change < -50] <- "> -50% Change"

combined$humidity_change <- combined$humidity_change %>%
                        replace(combined$humidity_change >= 0 & combined$humidity_change <= 10, "flat to +10%") %>%
                        replace(combined$humidity_change < 0 & combined$humidity_change >= -10, "flat to -10%") %>%
                        replace(combined$humidity_change < -10 & combined$humidity_change >= -25, "-10% to -25%") %>%
                        replace(combined$humidity_change < -25, "-25% to -50%")

combined$wind_change <- combined$wind_change %>%
                        replace(combined$wind_change >= 0 & combined$wind_change <= 10, "flat to +10%") %>%
                        replace(combined$wind_change < 0, "flat to -10%") %>%
                        replace(combined$wind_change > 10, "+10% to +25%")

#adding colors
library(RColorBrewer)
pal1 = colorFactor("Reds", reverse = TRUE, domain = combined$temp_change)
color_temp = pal1(combined$temp_change)
pal2 = colorFactor("Blue", reverse = TRUE, domain = combined$precip_change)
color_precip = pal2(combined$precip_change)
pal3 = colorFactor("YlOrBr", reverse = TRUE, domain = combined$humidity_change)
color_humidity = pal3(combined$humidity_change)
pal4 = colorFactor("Greys", reverse = TRUE, domain = combined$wind_change)
color_wind = pal4(combined$wind_change)
library(leaflet)

map1 <- combined %>% leaflet() %>% 
                     addTiles('http://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>% 
                     addCircles(color = color_temp, group = "Temperature") %>%
                     addCircles(color = color_precip, group = "Precipitation") %>%
                     addCircles(color = color_humidity, group = "Humidity") %>%
                     addCircles(color = color_wind, group = "Wind") %>%
                     setView(lng = 135, lat = -28, zoom = 4.1) %>%
                     addLayersControl(overlayGroups = c("Temperature", "Precipitation", "Humidity", 
                                                        "Wind")) %>%
                     clearBounds() %>%
                     addLegend(pal = pal1, values = combined$temp_change, title = "Temperature % Change", 
                               group = "Temperature") %>%
                     addLegend(pal = pal2, values = combined$precip_change, title = "Precipitation % Change", 
                               group = "Precipitation") %>%
                     addLegend(pal = pal3, values = combined$humidity_change, title = "Humidty % Change", 
                               group = "Humidity") %>%
                     addLegend(pal = pal4, values = combined$wind_change, title = "Wind % Change", 
                               group = "Wind")
Assuming "lon" and "lat" are longitude and latitude, respectively
Assuming "lon" and "lat" are longitude and latitude, respectively
Assuming "lon" and "lat" are longitude and latitude, respectively
Assuming "lon" and "lat" are longitude and latitude, respectively
map1

NA
---
title: "Weather Visualizations"
output: html_notebook
---
```{R}
library(tidyverse)
chart_melt <- read_csv("chart_melt.csv")


library(htmlwidgets)
library(plotly)

plot1 <- ggplot(chart_melt, aes(x=YEAR, y=weather_index, colour=variable)) +
          geom_point(aes(text=paste(metric,":", weather_value))) +
          geom_line() +
          facet_grid(~variable) +
          xlab("Year") + ylab("Weather Index") + ggtitle("Australia Weather Metrics 2010-2019") + 
          theme_minimal() +
          theme(legend.position = "none",
                plot.title = element_text(face = "bold", size = 20, hjust = 0.5), 
                axis.text.x = element_text(angle = 60, size = 8, hjust = 10),
                axis.title.x = element_text(vjust = 0.5)) +
          scale_x_continuous(breaks = c(2010, 2012, 2014, 2016, 2018))

ggplotly(plot1, tooltip = "text")
```

```{R}
dt <- read_csv("dt.csv")


library(DT)

FireSeason19_20 <- dt %>% datatable(rownames = FALSE, 
                                    colnames = c("Week", "Average Temperature (C)", "Total Precipitation (mm)", 
                                           "Average Humidity", "Max Wind (m/s)"),
                                    filter = c("top"), options = list(language = list(sSearch = "Filter:"))) %>%
                          formatRound(c('avg_temp', 'sum_precip', 'avg_humidity', 'max_wind'), 0)

FireSeason19_20 <- FireSeason19_20 %>% formatStyle('avg_temp',
                                                    background = styleColorBar(dt$avg_temp, 'tomato'),
                                                    backgroundPosition = 'right') %>%
                                       formatStyle('sum_precip',
                                                    background = styleColorBar(dt$sum_precip, 'lightblue'),
                                                    backgroundPosition = 'right') %>%
                                       formatStyle('avg_humidity',
                                                    background = styleColorBar(dt$avg_humidity, 'navajowhite'),
                                                    backgroundPosition = 'right') %>%
                                       formatStyle('max_wind',
                                                    background = styleColorBar(dt$max_wind, 'lightgrey'),
                                                    backgroundPosition = 'right')
FireSeason19_20

```

```{R}
combined <- read_csv("combined.csv")


#changing the buckets for each group
combined$temp_change[combined$temp_change > 25] <- "+25% to +50%"
combined$temp_change[combined$temp_change > 10 & combined$temp_change <= 25] <- "+10% to +25%"
combined$temp_change[combined$temp_change > 0  & combined$temp_change <= 10] <- "flat to +10%"
combined$temp_change[combined$temp_change == -1] <- "flat to -10%"
combined$temp_change[combined$temp_change == -3] <- "flat to -10%"
combined$temp_change[combined$temp_change == 9] <- "flat to +10%"
combined$temp_change[combined$temp_change == 8] <- "flat to +10%"
combined$temp_change[combined$temp_change == 7] <- "flat to +10%"
combined$temp_change[combined$temp_change == 6] <- "flat to +10%"
combined$temp_change[combined$temp_change == 5] <- "flat to +10%"
combined$temp_change[combined$temp_change == 4] <- "flat to +10%"
combined$temp_change[combined$temp_change == 3] <- "flat to +10%"

combined$precip_change[combined$precip_change < -50] <- "> -50% Change"

combined$humidity_change <- combined$humidity_change %>%
                        replace(combined$humidity_change >= 0 & combined$humidity_change <= 10, "flat to +10%") %>%
                        replace(combined$humidity_change < 0 & combined$humidity_change >= -10, "flat to -10%") %>%
                        replace(combined$humidity_change < -10 & combined$humidity_change >= -25, "-10% to -25%") %>%
                        replace(combined$humidity_change < -25, "-25% to -50%")

combined$wind_change <- combined$wind_change %>%
                        replace(combined$wind_change >= 0 & combined$wind_change <= 10, "flat to +10%") %>%
                        replace(combined$wind_change < 0, "flat to -10%") %>%
                        replace(combined$wind_change > 10, "+10% to +25%")

#adding colors
library(RColorBrewer)
pal1 = colorFactor("Reds", reverse = TRUE, domain = combined$temp_change)
color_temp = pal1(combined$temp_change)
pal2 = colorFactor("Blue", reverse = TRUE, domain = combined$precip_change)
color_precip = pal2(combined$precip_change)
pal3 = colorFactor("YlOrBr", reverse = TRUE, domain = combined$humidity_change)
color_humidity = pal3(combined$humidity_change)
pal4 = colorFactor("Greys", reverse = TRUE, domain = combined$wind_change)
color_wind = pal4(combined$wind_change)
```


```{R}
library(leaflet)

map1 <- combined %>% leaflet() %>% 
                     addTiles('http://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>% 
                     addCircles(color = color_temp, group = "Temperature") %>%
                     addCircles(color = color_precip, group = "Precipitation") %>%
                     addCircles(color = color_humidity, group = "Humidity") %>%
                     addCircles(color = color_wind, group = "Wind") %>%
                     setView(lng = 135, lat = -28, zoom = 4.1) %>%
                     addLayersControl(overlayGroups = c("Temperature", "Precipitation", "Humidity", 
                                                        "Wind")) %>%
                     clearBounds() %>%
                     addLegend(pal = pal1, values = combined$temp_change, title = "Temperature % Change", 
                               group = "Temperature") %>%
                     addLegend(pal = pal2, values = combined$precip_change, title = "Precipitation % Change", 
                               group = "Precipitation") %>%
                     addLegend(pal = pal3, values = combined$humidity_change, title = "Humidty % Change", 
                               group = "Humidity") %>%
                     addLegend(pal = pal4, values = combined$wind_change, title = "Wind % Change", 
                               group = "Wind")

map1

```



